Remember what you should do first when you start your R session? First we load the packages we will need.


#Load packages

library(readr)
library(dplyr)
library(ggplot2)


Start by reading in the data. The scrap data you will use is on the X-drive. It is a clean version of the scrap data we’ve been using. Remember, when reading in data from the X-drive, R reads forward slashes.

The X drive location of the file is “X:/Agency_Files/Outcomes/Risk_Eval_Air_Mod/_Air_Risk_Evaluation/R/R_Camp/Intro to R/RTrain - Star Wars/data/starwars_scrap_jakku_clean.csv“.

Notice that we are including comments in the R script so that your future self can follow along and see what you did.


Show solution 

#Read in data
clean_scrap <- read_csv("X:/Agency_Files/Outcomes/Risk_Eval_Air_Mod/_Air_Risk_Evaluation/R/R_Camp/Intro to R/RTrain - Star Wars/data/starwars_scrap_jakku_clean.csv")

head(clean_scrap)
## # A tibble: 6 x 6
##   items        origin    destination price_per_ton amount_tons total_price
##   <chr>        <chr>     <chr>               <dbl>       <dbl>       <dbl>
## 1 electrotele~ outskirts trade cara~         850.         868.     737981.
## 2 atmospheric~ craterto~ niima outp~          56.2      33978.    1909912.
## 3 bulkhead     craterto~ raiders            1005.         645.     647843.
## 4 main drive   blowback~ trade cara~         598.        1961.    1172184.
## 5 flight reco~ outskirts niima outp~         591.         887      524155.
## 6 proximity s~ outskirts raiders            1229.        7081     8702761.


Did it load successfully? Look in your environment. You should see “clean_scrap”. There should be 6 variables and 573 rows.


Take a couple of minutes to get an overview of the data. Open and look at your data in at least two ways. Do you remember some of the functions to do that?
1. Click on the data name in the environment to open the window. 1. Use glimpse() to look at your data.


Show solution 

#View the data
glimpse(clean_scrap)
## Observations: 573
## Variables: 6
## $ items         <chr> "electrotelescope", "atmospheric thrusters", "bu...
## $ origin        <chr> "outskirts", "cratertown", "cratertown", "blowba...
## $ destination   <chr> "trade caravan", "niima outpost", "raiders", "tr...
## $ price_per_ton <dbl> 849.79, 56.21, 1004.83, 597.85, 590.93, 1229.03,...
## $ amount_tons   <dbl> 868.4280, 33978.1545, 644.7285, 1960.6650, 887.0...
## $ total_price   <dbl> 737981.43, 1909912.06, 647842.54, 1172183.57, 52...


Look at a summary of your data using summary().


Show solution 

#View a summary of the data
summary(clean_scrap)
##     items              origin          destination       
##  Length:573         Length:573         Length:573        
##  Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character  
##                                                          
##                                                          
##                                                          
##  price_per_ton      amount_tons        total_price      
##  Min.   :  29.15   Min.   :    0.01   Min.   :       5  
##  1st Qu.: 314.23   1st Qu.:  238.99   1st Qu.:  128921  
##  Median : 629.28   Median : 1298.00   Median :  757656  
##  Mean   :1010.85   Mean   : 3724.23   Mean   : 3483802  
##  3rd Qu.:1329.05   3rd Qu.: 4678.44   3rd Qu.: 2631778  
##  Max.   :7211.01   Max.   :60116.67   Max.   :83712615


What if you only want to keep the items and amount_tons fields? Use select() to create a new data frame keeping only those columns and save it as an object called select_scrap.


Show solution 

select_scrap <- select(clean_scrap, items, amount_tons)


Order the data frame you just created by amount_tons from highest to lowest. Which item had the highest weight?


Show solution 

select_scrap <- arrange(select_scrap, desc(items))


Filter your select data set to all items with an amount higher than 1000. Call the dataset ‘filter_scrap’


Show solution 

filter_scrap <- filter(select_scrap, amount_tons > 1000)


Add a filter to to the amount_tons > 1000 dataset. Include only “proximity sensor” and “hyperdrive”


Show solution

You will need %in%, c() and filter.



Show solution 

filter_scrap <- filter(select_scrap, amount_tons > 1000,
                       items %in% c("proximity sensor", "hyperdrive"))


Add a column with your favorite Star Wars character to your filtered data frame.


Show solution 

filter_scrap <- mutate(filter_scrap, my_favorite_character = "Admiral Ackbar")


We’ve got all the amount of items in tons in our data set, and we have our favorite StarWars character, but we want to include the amount of items in pounds. Use mutate() to calculate the number of pounds in your filtered dataset. Call that column ‘amount_pounds’.



Show solution 

filter_scrap <- mutate(filter_scrap, amount_pounds = amount_tons * 2000)


We want to make a table of recommendations to our Junk Boss Unkar Plutt. In our filtered dataset, we want to buy scrap if if it is a Hyperdrive and ignore scrap if its a Proximity sensor. Remember, we filtered our table to only those two types of scrap. Use mutate() to make a column that reports “buy” if the item is a hyperdrive and “ignore” if the item is a proximity sensor. Call this new column do_this. You will need both ifelse() and mutate() for this task.



Show solution 

filter_scrap <- mutate(filter_scrap, do_this = ifelse(items == "hyperdrive", "buy", "ignore"))



> Let’s take a closer look at our full dataset now (clean_scrap). We want to give the Junk Boss a summary of all of this data. He doesn’t have the patience to really look at a lot of data. He hates numbers! He likes money. He wants to know the following things:

  1. The sum of all the money potentially earned by item.
  2. The maximum money potentially earned by item.
  3. The number of reports of each item.
  4. The 35th percentile of the price by item.

[We don’t even know how he learned about “quantile”, we are pretty sure someone told him about this just to test our abilities. If we don’t provide this summarized dataset, Unkar Plutt is likely going to shoot us into space rendering us…dead. We don’t want that. Let’s make a summary table.]



Hint:

You will need the pipe %>%, group_by(), summarise(), sum(), max(), quantile(), and n().




Hint # 2! 

summary_scrap <- clean_scrap %>%
                group_by() %>%
                summarise()




Show solution 

summary_scrap <- clean_scrap %>%
  group_by(items) %>%
  summarise(sum_price = sum(total_price),
            max_price = max(total_price),
            count_price = n(),
            price_35th = quantile(total_price, 0.35))


Oh boy, old Unkar just learned about plots. What will he want next? He wants a plot of the maximum total prices by item. We must create this plot or perish. Try both geom_col() and geom_point(0) to see which make a more simple look at the price maxima.



Show solution 

ggplot(data = summary_scrap, aes(items, max_price)) +
  geom_col()




Show solution 

ggplot(data = summary_scrap, aes(items, max_price)) +
  geom_point()


Try coord_flip() to make the plot more readable. If you’re interested in learning more about coord_flip(), ask R for help! ?coord_flip



Show solution 

ggplot(data = summary_scrap, aes(items, max_price)) +
  geom_col() +
  coord_flip()


This plot might look a lot better if the maximum data were sorted. Try reorder() to make this chart way more readable. Type “?reorder” to learn more about that function.



Show solution 

ggplot(data = summary_scrap, aes(reorder(items, max_price), max_price)) +
  geom_col() +
  coord_flip()


Nice work!! You may now move on to the Commodore level analysis.

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